You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process. Which service should you use?

• A.Dataflow
• B.Dataprep
• C.Apache Flink
• D.Cloud Data Fusion

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You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

• A.Import your user events and then your product catalog to make sure you have the highest quality event stream
• B.Use the "Other Products You May Like" recommendation type to increase the click-through rate
• C.Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.
• D.Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.

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You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?

• A.1. Create a new service account and grant it the Notebook Viewer role.
  2 Grant the Service Account User role to each team member on the service account.
  3 Grant the Vertex Al User role to each team member.
  4. Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.
• B.1. Grant the Vertex Al User role to the default Compute Engine service account.
  2. Grant the Service Account User role to each team member on the default Compute Engine service account.
  3. Provision a Vertex Al Workbench user-managed notebook instance that uses the default Compute Engine service account.
• C.1 Create a new service account and grant it the Vertex Al User role.
  2 Grant the Service Account User role to each team member on the service account.
  3. Grant the Notebook Viewer role to each team member.
  4 Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.
• D.1 Grant the Vertex Al User role to the primary team member.
  2. Grant the Notebook Viewer role to the other team members.
  3. Provision a Vertex Al Workbench user-managed notebook instance that uses the primary user's account.

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You are an AI architect at a popular photo-sharing social media platform. Your organization's content moderation team currently scans images uploaded by users and removes explicit images manually. You want to implement an AI service to automatically prevent users from uploading explicit images. What should you do?

• A.Develop a custom TensorFlow model in a Vertex AI Workbench instance. Train the model on a dataset of manually labeled images. Deploy the model to a Vertex AI endpoint. Run periodic batch inference to identify inappropriate uploads and report them to the content moderation team.
• B.Train an image clustering model using TensorFlow in a Vertex AI Workbench instance. Deploy this model to a Vertex AI endpoint and configure it for online inference. Run this model each time a new image is uploaded to identify and block inappropriate uploads.
• C.Create a dataset using manually labeled images. Ingest this dataset into AutoML. Train an image classification model and deploy it to a Vertex AI endpoint. Integrate this endpoint with the image upload process to identify and block inappropriate uploads. Monitor predictions and periodically retrain the model.
• D.Send a copy of every user-uploaded image to a Cloud Storage bucket. Configure a Cloud Run function that triggers the Cloud Vision API to detect explicit content each time a new image is uploaded. Report the classifications to the content moderation team for review.

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You work at a mobile gaming startup that creates online multiplayer games. Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated. Your model has performed well during testing, and you now need to deploy the model to production. You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

• A.Import the model into Vertex AI Model Registry. Create a Vertex AI endpoint that hosts the model, and make online inference requests.
• B.Save the model files in a Cloud Storage bucket. Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.
• C.Save the model files in a VM. Load the model files each time there is a prediction request, and run an inference job on the VM
• D.Import the model into Vertex AI Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.

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